Abstract
In this article we present an analysis of the WITT algorithm for conceptual clustering as proposed by Hanson and Bauer (1989). We show that the measures proposed for the original WITT algorithm have serious shortcomings. We propose some alternatives for these measures, and, moreover, we make a further analysis of these alternatives such that setting the required thresholds will be less dependent of the characteristics of the cases that are to be clustered.
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References
Hanson, S.J., & Bauer, M. (1989). Conceptual clustering, categorization and polymorphy. Machine Learning, 3, 343–372.
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Talmon, J.L., Fonteijn, H. & Braspenning, P.J. An Analysis of the WITT Algorithm. Machine Learning 11, 91–104 (1993). https://doi.org/10.1023/A:1022683203002
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DOI: https://doi.org/10.1023/A:1022683203002